






"""simple docstring"""


import argparse
import json
import os
from collections import OrderedDict

import torch

from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken


@torch.no_grad()
def        UpperCAmelCase      (					a__     ,      a__     ,      a__     ,      a__     ,      a__ ):





						'''simple docstring'''






						with open(a__ ) as metadata_file:
												lowerCAmelCase   :Any						=			json.load(a__ )
						lowerCAmelCase   :List[Any]						=			LukeConfig(use_entity_aware_attention=a__     ,      **metadata['model_config'] )

						# Load in the weights from the checkpoint_path
						lowerCAmelCase   :int						=			torch.load(a__     ,      map_location='cpu' )['module']

						# Load the entity vocab file
						lowerCAmelCase   :Tuple						=			load_original_entity_vocab(a__ )
						# add an entry for [MASK2]
						lowerCAmelCase   :str						=			max(entity_vocab.values() ) + 1
						config.entity_vocab_size += 1

						lowerCAmelCase   :int						=			XLMRobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name'] )

						# Add special tokens to the token vocabulary for downstream tasks
						lowerCAmelCase   :Optional[int]						=			AddedToken('<ent>'     ,      lstrip=a__     ,      rstrip=a__ )
						lowerCAmelCase   :Union[str, Any]						=			AddedToken('<ent2>'     ,      lstrip=a__     ,      rstrip=a__ )
						tokenizer.add_special_tokens({'additional_special_tokens': [entity_token_a, entity_token_a]} )
						config.vocab_size += 2

						print(F"""Saving tokenizer to {pytorch_dump_folder_path}""" )
						tokenizer.save_pretrained(a__ )
						with open(os.path.join(a__     ,      'tokenizer_config.json' )     ,      'r' ) as f:
												lowerCAmelCase   :Union[str, Any]						=			json.load(a__ )
						lowerCAmelCase   :Tuple						=			'MLukeTokenizer'
						with open(os.path.join(a__     ,      'tokenizer_config.json' )     ,      'w' ) as f:
												json.dump(a__     ,      a__ )

						with open(os.path.join(a__     ,      MLukeTokenizer.vocab_files_names['entity_vocab_file'] )     ,      'w' ) as f:
												json.dump(a__     ,      a__ )

						lowerCAmelCase   :Optional[int]						=			MLukeTokenizer.from_pretrained(a__ )

						# Initialize the embeddings of the special tokens
						lowerCAmelCase   :str						=			tokenizer.convert_tokens_to_ids(['@'] )[0]
						lowerCAmelCase   :Union[str, Any]						=			tokenizer.convert_tokens_to_ids(['#'] )[0]

						lowerCAmelCase   :Any						=			state_dict['embeddings.word_embeddings.weight']
						lowerCAmelCase   :Dict						=			word_emb[ent_init_index].unsqueeze(0 )
						lowerCAmelCase   :Union[str, Any]						=			word_emb[enta_init_index].unsqueeze(0 )
						lowerCAmelCase   :Optional[int]						=			torch.cat([word_emb, ent_emb, enta_emb] )
						# add special tokens for 'entity_predictions.bias'
						for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
												lowerCAmelCase   :List[Any]						=			state_dict[bias_name]
												lowerCAmelCase   :List[Any]						=			decoder_bias[ent_init_index].unsqueeze(0 )
												lowerCAmelCase   :int						=			decoder_bias[enta_init_index].unsqueeze(0 )
												lowerCAmelCase   :Union[str, Any]						=			torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias] )

						# Initialize the query layers of the entity-aware self-attention mechanism
						for layer_index in range(config.num_hidden_layers ):
												for matrix_name in ["query.weight", "query.bias"]:
																		lowerCAmelCase   :Optional[int]						=			F"""encoder.layer.{layer_index}.attention.self."""
																		lowerCAmelCase   :List[Any]						=			state_dict[prefix + matrix_name]
																		lowerCAmelCase   :List[str]						=			state_dict[prefix + matrix_name]
																		lowerCAmelCase   :Tuple						=			state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
						lowerCAmelCase   :int						=			state_dict['entity_embeddings.entity_embeddings.weight']
						lowerCAmelCase   :Union[str, Any]						=			entity_emb[entity_vocab['[MASK]']].unsqueeze(0 )
						lowerCAmelCase   :int						=			torch.cat([entity_emb, entity_mask_emb] )
						# add [MASK2] for 'entity_predictions.bias'
						lowerCAmelCase   :str						=			state_dict['entity_predictions.bias']
						lowerCAmelCase   :List[Any]						=			entity_prediction_bias[entity_vocab['[MASK]']].unsqueeze(0 )
						lowerCAmelCase   :Dict						=			torch.cat([entity_prediction_bias, entity_mask_bias] )

						lowerCAmelCase   :Union[str, Any]						=			LukeForMaskedLM(config=a__ ).eval()

						state_dict.pop('entity_predictions.decoder.weight' )
						state_dict.pop('lm_head.decoder.weight' )
						state_dict.pop('lm_head.decoder.bias' )
						lowerCAmelCase   :Union[str, Any]						=			OrderedDict()
						for key, value in state_dict.items():
												if not (key.startswith('lm_head' ) or key.startswith('entity_predictions' )):
																		lowerCAmelCase   :int						=			state_dict[key]
												else:
																		lowerCAmelCase   :str						=			state_dict[key]

						lowerCAmelCase	, lowerCAmelCase   :Any						=			model.load_state_dict(a__     ,      strict=a__ )

						if set(a__ ) != {"luke.embeddings.position_ids"}:
												raise ValueError(F"""Unexpected unexpected_keys: {unexpected_keys}""" )
						if set(a__ ) != {
						    "lm_head.decoder.weight",
						    "lm_head.decoder.bias",
						    "entity_predictions.decoder.weight",
						}:
												raise ValueError(F"""Unexpected missing_keys: {missing_keys}""" )

						model.tie_weights()
						assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
						assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()

						# Check outputs
						lowerCAmelCase   :Union[str, Any]						=			MLukeTokenizer.from_pretrained(a__     ,      task='entity_classification' )

						lowerCAmelCase   :int						=			'ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'
						lowerCAmelCase   :Optional[int]						=			(0, 9)
						lowerCAmelCase   :Optional[int]						=			tokenizer(a__     ,      entity_spans=[span]     ,      return_tensors='pt' )

						lowerCAmelCase   :Any						=			model(**a__ )

						# Verify word hidden states
						if model_size == "large":
												raise NotImplementedError
						else:  # base
												lowerCAmelCase   :Any						=			torch.Size((1, 33, 7_68) )
												lowerCAmelCase   :List[str]						=			torch.tensor([[0.0_892, 0.0_596, -0.2_819], [0.0_134, 0.1_199, 0.0_573], [-0.0_169, 0.0_927, 0.0_644]] )

						if not (outputs.last_hidden_state.shape == expected_shape):
												raise ValueError(
												    F"""Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}""" )
						if not torch.allclose(outputs.last_hidden_state[0, :3, :3]     ,      a__     ,      atol=1e-4 ):
												raise ValueError

						# Verify entity hidden states
						if model_size == "large":
												raise NotImplementedError
						else:  # base
												lowerCAmelCase   :List[Any]						=			torch.Size((1, 1, 7_68) )
												lowerCAmelCase   :Union[str, Any]						=			torch.tensor([[-0.1_482, 0.0_609, 0.0_322]] )

						if not (outputs.entity_last_hidden_state.shape == expected_shape):
												raise ValueError(
												    F"""Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"""
												    F""" {expected_shape}""" )
						if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3]     ,      a__     ,      atol=1e-4 ):
												raise ValueError

						# Verify masked word/entity prediction
						lowerCAmelCase   :Union[str, Any]						=			MLukeTokenizer.from_pretrained(a__ )
						lowerCAmelCase   :Dict						=			'Tokyo is the capital of <mask>.'
						lowerCAmelCase   :Tuple						=			(24, 30)
						lowerCAmelCase   :Tuple						=			tokenizer(a__     ,      entity_spans=[span]     ,      return_tensors='pt' )

						lowerCAmelCase   :Optional[int]						=			model(**a__ )

						lowerCAmelCase   :Tuple						=			encoding['input_ids'][0].tolist()
						lowerCAmelCase   :Dict						=			input_ids.index(tokenizer.convert_tokens_to_ids('<mask>' ) )
						lowerCAmelCase   :Union[str, Any]						=			outputs.logits[0][mask_position_id].argmax(dim=-1 )
						assert "Japan" == tokenizer.decode(a__ )

						lowerCAmelCase   :List[str]						=			outputs.entity_logits[0][0].argmax().item()
						lowerCAmelCase   :Tuple						=			[
						    entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
						]
						assert [e for e in multilingual_predicted_entities if e.startswith('en:' )][0] == "en:Japan"

						# Finally, save our PyTorch model and tokenizer
						print('Saving PyTorch model to {}'.format(a__ ) )
						model.save_pretrained(a__ )


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						lowerCAmelCase   :List[Any]						=			['[MASK]', '[PAD]', '[UNK]']

						lowerCAmelCase   :Optional[int]						=			[json.loads(a__ ) for line in open(a__ )]

						lowerCAmelCase   :str						=			{}
						for entry in data:
												lowerCAmelCase   :List[str]						=			entry['id']
												for entity_name, language in entry["entities"]:
																		if entity_name in SPECIAL_TOKENS:
																								lowerCAmelCase   :Any						=			entity_id
																								break
																		lowerCAmelCase   :Optional[Any]						=			F"""{language}:{entity_name}"""
																		lowerCAmelCase   :Optional[Any]						=			entity_id
						return new_mapping


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE   =   argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('--checkpoint_path', type=str, help='Path to a pytorch_model.bin file.')
		parser.add_argument(
		    '--metadata_path', default=None, type=str, help='Path to a metadata.json file, defining the configuration.'
		)
		parser.add_argument(
		    '--entity_vocab_path',
		    default=None,
		    type=str,
		    help='Path to an entity_vocab.tsv file, containing the entity vocabulary.',
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, help='Path to where to dump the output PyTorch model.'
		)
		parser.add_argument(
		    '--model_size', default='base', type=str, choices=['base', 'large'], help='Size of the model to be converted.'
		)
		__SCREAMING_SNAKE_CASE   =   parser.parse_args()
		convert_luke_checkpoint(
		    args.checkpoint_path,
		    args.metadata_path,
		    args.entity_vocab_path,
		    args.pytorch_dump_folder_path,
		    args.model_size,
		)







"""simple docstring"""


from abc import ABC, abstractmethod
from argparse import ArgumentParser




class 						__UpperCamelCase     (     UpperCamelCase						):
					@staticmethod
					@abstractmethod
					def        UpperCAmelCase__ (							UpperCAmelCase							:	ArgumentParser   )	->       Optional[Any]:
											raise NotImplementedError()
					@abstractmethod
					def        UpperCAmelCase__ (							self							:	str   )	->       List[str]:
											raise NotImplementedError()